Step 7: Item-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count - Getting Started | Xineoh

Introduction

How It Works

Prerequisites

Administrator Account and Organisation Creation Application Creation Data Set Requirements

Step 1: Data Cleanup Step 2: Data Transformation Step 3: Upload to S3 Bucket Step 4: Sharing S3 Bucket Access to Xineoh’s API

Applications, Datasets and Schemas

Applications

Application Types

Datasets and Schemas

Minimum Requirements for Datasets and Schemas Customising Datasets and Schemas

Steps in Building a Recommender with the API

Step 1: Batch Upload Step 2: Build Data and Structures for Training and Testing Step 3: Train Model Step 4: Test Model Step 5: Optimise Meta Parameters Step 6: User-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 7: Item-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 8: Get Recommendations

Sample Implementation - Movielens 25M Dataset

S3 Bucket Creation Login as Organisation First Application Download MovieLens Preprocessed Data Upload Data to S3 Bucket Building the Recommender

Uploading S3 Bucket data to Xineoh Platform Build Data Train Test Optimization User Clustering and Trade-Off Item Clustering and Trade-Off

First Recommendations

Open Navigation

Step 7: Item-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count

Step 7.1: Cluster Items

This API call executes the clustering using the latent factors/embeddings trained in the recommender. You select the minimum and the maximum number of item-clusters to be tested for their MSEs (Mean Square Errors). This will be used to choose the optimal number of item-clusters for your organisation.

For more information, please refer to the API documentation.

Step 7.2: Trade-Off Item Chart

This API call gets the mean squared error for each item-cluster count between the specified min and max.

The API will return an MSE for each cluster count. We can then use this data to decide how many clusters we want to set in the upcoming API call.

For more information, please refer to the API documentation.

Step 7.3: Choosing Item Cluster Count based on MSE Results

Once you have looked at the clusters' MSE results, you can decide how many item clusters are optimal. Usually, the number of clusters where the drop-off in MSE elbows is selected.

For more information, please refer to the API documentation.

Previous Next